High-Performance Multi-Object Tracking for Autonomous Driving in Urban Scenarios With Heterogeneous Embedded Boards

Abstract

Autonomous Driving is emerging as a paradigm shift in the way we conceive people and goods transportation. It promises to improve road safety, reduce traffic congestion, and increase overall transportation efficiency. It is made possible by a plethora of modern technologies, such as AI, low-power hardware, and complex computing. Common stages of Autonomous Driving systems are the identification of objects in the scene (Object Detection), and the ability to predict the evolution of the tracked objects’ states – usually, positions and velocities (Multi-Object Tracking). In this paper, we address the issues behind high-performance Multi-Object Tracking (MOT) algorithms for a real-world urban transportation scenario. The main objective is to exploit the high-performance capabilities of NVIDIA heterogeneous embedded platforms, which are not automatically compatible with the peculiar features of these algorithms. We propose and discuss some non-trivial software design choices and implementation strategies that are needed to match the specific computational and memory access needs of MOT strategies, and the architectural opportunities of NVIDIA embedded GPGPU systems. Our code is made available as open-source. We compare our solution against a highly optimized multi-core version and show that we are able to trigger significant performance speedups (up to $7.19\times $ ) in all power-modes, sometimes even surpassing the CPU reference with the GPU in a lower-power operating mode.

Keywords

• Autonomous driving
• cyber-physical system
• CUDA
• heterogeneous board
• Kalman filter
• multi-object tracking